System and Technique to Estimate Physical Propagation Parameters Associated with a Seismic Survey

ABSTRACT

A technique includes estimating propagation parameters that are associated with a towed seismic survey based at least in part on seismic signal measurements and noise measurements.

BACKGROUND

The invention generally relates to a system and technique to estimatephysical propagation parameters that are associated with a seismicsurvey.

Seismic exploration involves surveying subterranean geologicalformations for hydrocarbon deposits. A survey typically involvesdeploying seismic source(s) and seismic sensors at predeterminedlocations. The sources generate seismic waves, which propagate into thegeological formations creating pressure changes and vibrations alongtheir way. Changes in elastic properties of the geological formationscatter the seismic waves, changing their direction of propagation andother properties. Part of the energy that is emitted by the sourcesreaches the seismic sensors. Some seismic sensors are sensitive topressure changes (hydrophones), others to particle motion (e.g.,geophones), and industrial surveys may deploy only one type of sensorsor both. In response to the detected seismic events, the sensorsgenerate electrical signals to produce seismic data. Analysis of theseismic data can then indicate the presence or absence of probablelocations of hydrocarbon deposits.

Some surveys are known as “marine” surveys because they are conducted inmarine environments. However, “marine” surveys may be conducted not onlyin saltwater environments, but also in fresh and brackish waters. In onetype of marine survey, called a “towed-array” survey, an array ofseismic sensor-containing streamers and sources is towed behind a surveyvessel.

SUMMARY

In an embodiment of the invention, a technique includes estimatingpropagation parameters that are associated with a towed seismic surveybased at least in part on seismic signal measurements and noisemeasurements.

In another embodiment of the invention, a system includes an interfaceand a processor. The interface receives data that is indicative ofseismic signal measurements and noise measurements. The processorprocesses the data to estimate propagation parameters that areassociated with a towed seismic survey based at least in part on theseismic signal measurements and the noise measurements.

In yet another embodiment of the invention, an article includes acomputer accessible storage medium to store instructions that whenexecuted by a processor-based system cause the processor-based system toobtain data indicative of seismic signal measurements and noisemeasurements, and process the data to estimate propagation parametersthat are associated with a towed seismic survey based at least in parton the seismic signal measurements and the noise measurements.

Advantages and other features of the invention will become apparent fromthe following drawing, description and claims.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a schematic diagram of a marine seismic acquisition systemaccording to an embodiment of the invention.

FIGS. 2 and 3 are flow diagrams depicting techniques to estimatepropagation parameters associated with a towed seismic survey accordingto embodiments of the invention.

FIG. 4 is a schematic diagram of a seismic data processing systemaccording to an embodiment of the invention.

DETAILED DESCRIPTION

FIG. 1 depicts an embodiment 10 of a marine seismic data acquisitionsystem in accordance with some embodiments of the invention. In thesystem 10, a survey vessel 20 tows one or more seismic streamers 30 (twoexemplary streamers 30 being depicted in FIG. 1) behind the vessel 20.The seismic streamers 30 may be several thousand meters long and maycontain various support cables (not shown), as well as wiring and/orcircuitry (not shown) that may be used to support communication alongthe streamers 30.

Each seismic streamer 30 contains seismic sensors, which record seismicsignals. In accordance with some embodiments of the invention, theseismic sensors are multi-component seismic sensors 58, each of which iscapable of detecting a pressure wavefield and at least one component ofa particle motion that is associated with acoustic signals that areproximate to the multi-component seismic sensor 58. Examples of particlemotions include one or more components of a particle displacement, oneor more components (inline (x), crossline (y) and vertical (z)components (see axes 59, for example)) of a particle velocity and one ormore components of a particle acceleration.

Depending on the particular embodiment of the invention, themulti-component seismic sensor 58 may include one or more hydrophones,geophones, particle displacement sensors, particle velocity sensors,accelerometers, pressure gradient sensors, or combinations thereof.

For example, in accordance with some embodiments of the invention, aparticular multi-component seismic sensor 58 may include a hydrophone 55for measuring pressure and three orthogonally-aligned accelerometers 50to measure three corresponding orthogonal components of particlevelocity and/or acceleration near the seismic sensor 58. It is notedthat the multi-component seismic sensor 58 may be implemented as asingle device (as depicted in FIG. 1) or may be implemented as aplurality of devices, depending on the particular embodiment of theinvention. A particular multi-component seismic sensor 58 may alsoinclude pressure gradient sensors 56, which constitute another type ofparticle motion sensor. The pressure gradient sensor measures the changein the pressure wavefield at a particular point with respect to aparticular direction. For example, one of the pressure gradient sensors56 may acquire seismic data indicative of, at a particular point, thepartial derivative of the pressure wavefield with respect to thecrossline direction, and another one of the pressure gradient sensorsmay acquire, at a particular point, seismic data indicative of thepressure data with respect to the inline direction.

The marine seismic data acquisition system 10 includes one or moreseismic sources 40 (one exemplary source 40 being depicted in FIG. 1),such as air guns and the like. In some embodiments of the invention, theseismic sources 40 may be coupled to, or towed by, the survey vessel 20.Alternatively, in other embodiments of the invention, the seismicsources 40 may operate independently of the survey vessel 20, in thatthe sources 40 may be coupled to other vessels or buoys, as just a fewexamples.

As the seismic streamers 30 are towed behind the survey vessel 20,acoustic signals 42 (an exemplary acoustic signal 42 being depicted inFIG. 1), often referred to as “shots,” are produced by the seismicsources 40 and are directed down through a water column 44 into strata62 and 68 beneath a water bottom surface 24. The acoustic signals 42 arereflected from the various subterranean geological formations, such asan exemplary formation 65 that is depicted in FIG. 1.

The incident acoustic signals 42 that are acquired by the sources 40produce corresponding reflected acoustic signals, or pressure waves 60,which are sensed by the multi-component seismic sensors 58. It is notedthat the pressure waves that are received and sensed by themulti-component seismic sensors 58 include “up going” pressure wavesthat propagate to the sensors 58 without reflection, as well as “downgoing” pressure waves that are produced by reflections of the pressurewaves 60 from an air-water boundary 31.

The multi-component seismic sensors 58 generate signals (digitalsignals, for example), called “traces,” which indicate the acquiredmeasurements of the pressure wavefield and particle motion. The tracesare recorded and may be at least partially processed by a signalprocessing unit 23 that is deployed on the survey vessel 20, inaccordance with some embodiments of the invention. For example, aparticular multi-component seismic sensor 58 may provide a trace, whichcorresponds to a measure of a pressure wavefield by its hydrophone 55;and the sensor 58 may provide one or more traces that correspond to oneor more components of particle motion, which are measured by itsaccelerometers 50.

The goal of the seismic acquisition is to build up an image of a surveyarea for purposes of identifying subterranean geological formations,such as the exemplary geological formation 65. Subsequent analysis ofthe representation may reveal probable locations of hydrocarbon depositsin subterranean geological formations. Depending on the particularembodiment of the invention, portions of the analysis of therepresentation may be performed on the seismic survey vessel 20, such asby the signal processing unit 23. In accordance with other embodimentsof the invention, the representation may be processed by a seismic dataprocessing system (such as an exemplary seismic data processing system320 that is depicted in FIG. 4 and is further described below) that maybe, for example, located on land or on the vessel 20. Thus, manyvariations are possible and are within the scope of the appended claims.

The down going pressure waves create an interference known as “ghost” inthe art. Depending on the incidence angle of the up going wavefield andthe depth of the streamer 30, the interference between the up going anddown going wavefields creates nulls, or notches, in the recordedspectrum. These notches may reduce the useful bandwidth of the spectrumand may limit the possibility of towing the streamers 30 in relativelydeep water (water greater than 20 meters (m), for example).

The technique of decomposing the recorded wavefield into up and downgoing components is often referred to as wavefield separation, or“deghosting.” The particle motion data that is provided by themulti-component seismic sensors 58 allows the recovery of “ghost” freedata, which means data that is indicative of the upgoing wavefield.

Many seismic data processing algorithms use a physical propagationmodel, which is characterized by physical propagation parameters. Theseparameters may include, as examples, streamer depth, acoustic soundvelocity and the reflection coefficient of the sea surface.

As a more specific example, a deghosting algorithm is a seismic dataprocessing algorithm that relies on the physical propagation model, suchas the one described, for example, in U.S. patent application Ser. No.11/740,641, entitled, “Method for Optimal Wave Field Separation,” whichwas filed on Apr. 25, 2007 (Attorney Docket No. 14.0327). The deghostingalgorithm provides an estimate of the upgoing pressure wavefield in atowed marine seismic survey when measurements from a vector streamer(i.e., a streamer providing particle motion and pressure measurements)are available. The deghosting algorithm may rely on a measurement modelthat is described as follows:

M=HP _(u) +N   Eq. 1

where “M,” “H,” and “N” represent measurement, transfer function andmeasurement noise, vectors, respectively; and “Pu” represents theupgoing pressure wavefield. More specifically, the M, H and N vectorsmay be described as follows:

M=[P V_(x) V_(y) V_(z)]^(T),   Eq. 2

$\begin{matrix}{H = \lbrack {( {1 + \xi_{e}^{j\; 4\pi \; k_{z}z_{s}}} )\frac{{ck}_{x}}{f}( {1 + \xi_{e}^{j\; 4\pi \; k_{z}z_{s}}} )\frac{{ck}_{y}}{f}( {1 + \xi_{e}^{j\; 4\pi \; k_{z}z_{s}}} )\frac{{ck}_{z}}{f}( {1 - \xi_{e}^{j\; 4\pi \; k_{z}z_{s}}} )} \rbrack^{T}} & {{Eq}.\mspace{14mu} 3}\end{matrix}$

N=[N_(p) N_(x) N_(y) N_(z)]^(T),   Eq. 4

where the superscript “T” represents the transpose operation, “P”represents the pressure measurement; “V_(x),” “V_(y)” and “V_(z),”represents the inline, crossline and vertical components of the particlevelocities; “N_(p)” represents the noise on pressure measurement; and“N_(x),” “N_(y),” and “N_(z),” represents the respective measurementnoises on particle velocity measurements. Regarding the propagationparameters of the H vector, “ξ” represents the reflection coefficient ofthe sea surface; “z_(s)” represents the streamer depth; “c” representsthe acoustic speed of sound in water; “k_(z)” represents the verticalwavenumber; and “f” represents the frequency. The vertical wavenumberk_(z) is related to the horizontal wavenumbers k_(x), k_(y) and thefrequency f through the following relation:

$\begin{matrix}{k_{z} = {\frac{f}{c}{\sqrt{1 - {c^{2}\frac{k_{x}^{2} + k_{y}^{2}}{f^{2}}}}.}}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$

For purposes of deghosting the pressure wavefield, the optimal estimate(called “{circumflex over (P)}_(u)”) of the upgoing pressure wavefieldmay be expressed as follows:

$\begin{matrix}{{{\hat{P}}_{u} = {\frac{H^{T}C^{- 1}}{H^{T}C^{- 1}H}M}},} & {{Eq}.\mspace{14mu} 6}\end{matrix}$

where “C” represents the noise covariance matrix, which is defined asfollows:

C=E[NN^(T)].   Eq. 7

In Eq. 7, “E[ ]” represents the statistical expectation operator. It isnoted the covariance matrix C may thus be computed from the noise vectorN, and the noise vector N may be derived, for example, using an estimateof a portion of the particle motion/pressure measurement, which containsonly the noise record.

As shown by Eq. 6, the deghosting algorithm is an example of manyseismic data processing algorithms, which depend on the determination ofthe physical propagation parameters, which characterize the vector H(see Eq. 3). One technique to determine the physical propagationparameters involves minimizing the “cross ghost.” The cross ghost isdefined as the signal obtained by applying a pressure ghost operator onthe vertical velocity measurements; applying the corresponding verticalvelocity ghost operator on the pressure measurement; and computing thedifference. In the absence of noise and in a data window after thedirect arrival, the cross ghost is zero when the ghost operator matchesto the correct physical propagation channel. Otherwise, the cross ghostis non-zero. A difficulty with this approach, however, is that theeffect of the noise is not considered, which may be present in thevelocity and pressure measurements. In this regard, when one measurementcontains significantly more noise than the other measurement, thecorrect propagation parameters do not constitute the minima of the crossghost. As a result, the obtained estimates are usually biased, and thus,incorrect.

In accordance with embodiments of the invention, which are describedherein, the propagation parameters for a marine survey are determined bytaking into account the effect of noise in the measurements. Morespecifically, referring to FIG. 2, a technique 100 to determine thepropagation parameters includes obtaining (block 104) particle motionand pressure measurements and obtaining (block 108) noise measurements.The propagation parameters are estimated based at least in part on theparticle motion, pressure and noise measurements, pursuant to block 112.

Turning now to the more specific details, for purposes of estimating thepropagation parameters, a cost function called “J(α)” is defined interms of the measurement vector M, the noise covariance matrix C and theunknown transfer function vector H, as set forth below:

$\begin{matrix}{{J(\alpha)} = {{M^{T}C^{- 1}M} - {\frac{{{M^{T}C^{- 1}{H(\alpha)}}}^{2}}{{H(\alpha)}^{T}C^{- 1}{H(\alpha)}}.}}} & {{Eq}.\mspace{14mu} 8}\end{matrix}$

In Eq. 8, the cost function J is expressed as a function of an unknownparameter vector (called “α”), which parameterizes the vector H,described above in Eq. 3. As a specific example, the vector α maycontain such propagation parameters as the streamer depth z_(s), theacoustic speed of sound c and/or the reflection coefficient ξ (as just afew examples). Depending on the information available and/or possiblyother factors, the α vector may include only these parameters, mayinclude fewer parameters, or may include more parameters. Thus, manyvariations are possible and are within the scope of the appended claims.

For purposes of simplifying the J cost function, a singular valuedecomposition of the covariance matrix may be expressed as follows:

C=UΛU^(T),   Eq. 9

where “U” represents an orthogonal matrix, and “Λ” represents a diagonalmatrix. Assume the following definition of matrices:

M=U^(T)M, and   Eq. 10

H=U^(T)H .   Eq. 11

For these definitions, the cost function J(a) may be expressed asfollows:

$\begin{matrix}{{{J(\alpha)} = \frac{\sum\limits_{i < j}\frac{{{{{{\overset{\_}{H}}_{j}(\alpha)}{\overset{\_}{M}}_{i}} - {{{\overset{\_}{H}}_{i}(\alpha)}{\overset{\_}{M}}_{j}}}}^{2}}{\sigma_{i}^{2}\sigma_{j}^{2}}}{\sum\limits_{i}\frac{{{{\overset{\_}{H}}_{i}(\alpha)}}^{2}}{\sigma_{i}^{2}}}},} & {{Eq}.\mspace{14mu} 12}\end{matrix}$

where “ H _(j)(α)” and “ M _(j)” represent the j-th entry of the vectors“ H(α)” and “ M”, respectively; and “σ_(j) ²” represents the j-thdiagonal entry of the diagonal matrix Λ.

The expected value of the cost function J, E[J(α)], equals one when thevector α is the correct model parameter vector and E[J(α′)] is greaterthan one for any other α′, giving way to the following relationship:

$\begin{matrix}{{{E\lbrack {J( \alpha^{\prime} )} \rbrack} = {{1 + {\frac{1}{2}{P_{u}}^{2}\frac{\sum\limits_{i \neq j}\frac{{{{{{\overset{\_}{H}}_{i}( \alpha^{\prime} )}{{\overset{\_}{H}}_{j}(\alpha)}} - {{{\overset{\_}{H}}_{j}( \alpha^{\prime} )}{{\overset{\_}{H}}_{i}(\alpha)}}}}^{2}}{\sigma_{i}^{2}\sigma_{j}^{2}}}{\sum\limits_{i}\frac{{{{\overset{\_}{H}}_{i}( \alpha^{\prime} )}}^{2}}{\sigma_{i}^{2}}}}} > 1}},} & {{Eq}.\mspace{14mu} 13}\end{matrix}$

for α′≠α.

Having established the fact that E[J(α)] is minimized only when thecorrect model parameters are used, an estimator for the parameter vectorα may be expressed as follows:

$\begin{matrix}{\hat{\alpha} = {{\text{arg}\; {\min\limits_{\alpha}{E\lbrack {J(\alpha)} \rbrack}}} = \frac{\sum\limits_{i < j}\frac{E\lfloor {{{{{\overset{\_}{H}}_{j}(\alpha)}{\overset{\_}{M}}_{i}} - {{{\overset{\_}{H}}_{i}(\alpha)}{\overset{\_}{M}}_{j}}}}^{2} \rfloor}{\sigma_{i}^{2}\sigma_{j}^{2}}}{\sum\limits_{i}\frac{{{{\overset{\_}{H}}_{i}(\alpha)}}^{2}}{\sigma_{i}^{2}}}}} & {{Eq}.\mspace{14mu} 14}\end{matrix}$

In accordance with some embodiments of the invention, the statisticalexpectation operation may be approximated by averaging the results ofseveral measurement realizations. If a single realization of themeasurement vector M is used, the statistical expectation operation maybe approximated as follows:

$\begin{matrix}{{E\lbrack {J(\alpha)} \rbrack} \approx {\underset{f,k_{x},k_{y}}{\int{\int\int}}\frac{\sum\limits_{i < j}\frac{{{{{{\overset{\_}{H}}_{j}(\alpha)}{\overset{\_}{M}}_{i}} - {{{\overset{\_}{H}}_{i}(\alpha)}{\overset{\_}{M}}_{j}}}}^{2}}{\sigma_{i}^{2}\sigma_{j}^{2}}}{\sum\limits_{i}\frac{{{{\overset{\_}{H}}_{i}(\alpha)}}^{2}}{\sigma_{i}^{2}}}{f}{k_{x}}{{k_{y}}.}}} & {{Eq}.\mspace{14mu} 15}\end{matrix}$

Equation 15 represents an approximation to the ensemble average (i.e.,the statistical expectation operation) by frequency and wavenumberaverage. For purposes of reducing edge effects, the integrand of Eq. 15may be convolved (represented by the operator “*”) with a low passfunction (called “W” below) before integration, as described below:

$\begin{matrix}{{E\lbrack {J(\alpha)} \rbrack} \approx {\underset{f,k_{x},k_{y}}{\int{\int{\int W^{*}}}}\frac{\sum\limits_{i < j}\frac{{{{{{\overset{\_}{H}}_{j}(\alpha)}{\overset{\_}{M}}_{i}} - {{{\overset{\_}{H}}_{i}(\alpha)}{\overset{\_}{M}}_{j}}}}^{2}}{\sigma_{i}^{2}\sigma_{j}^{2}}}{\sum\limits_{i}\frac{{{{\overset{\_}{H}}_{i}(\alpha)}}^{2}}{\sigma_{i}^{2}}}{f}{k_{x}}{{k_{y}}.}}} & {{Eq}.\mspace{14mu} 16}\end{matrix}$

To summarize, in accordance with embodiments of the invention, atechnique 120 that is depicted in FIG. 3 may be used. The technique 120includes providing a cost function that is expressed in terms ofpressure, particle motion and noise measurements, pursuant to block 124.The cost function is inverted (block 128) for the propagationparameters.

It is noted that the techniques that are disclosed herein may be used in“over/under” applications (applications that involve towing streamers atdifferent depths) and “sea bed” applications (applications that involveplacing seismic sensors on the sea bed).

Other variations are contemplated and are within the scope of theappended claims. For example, modifications may be made to the costfunction to derive the same set of propagation parameters. For example,rather than minimizing the cost function that is set forth above in Eq.8 and 12, the cost function may be changed and correspondinglymaximized. Eq. 17 below sets forth one example of a cost function(called “J′(α)” below) that may be maximized (instead of minimized) toderive the propagation parameters:

$\begin{matrix}{{J^{\prime}(\alpha)} = {\frac{{{M^{T}C^{- 1}{H(\alpha)}}}^{2}}{{H(\alpha)}^{T}C^{- 1}{H(\alpha)}}.}} & {{Eq}.\mspace{14mu} 17}\end{matrix}$

It is noted that the techniques that are described herein may be usedfor calibration purposes, for instance, when the sensitivity of onemeasurement (e.g., particle motion data) is consistently different thanthe other set (e.g., pressure data). Thus, many variations andapplications are contemplated and are within the scope of the appendedclaims.

Referring to FIG. 4, in accordance with some embodiments of theinvention, a seismic data processing system 320 may perform thetechniques that are disclosed herein for purposes of determining thephysical propagation parameters of a marine survey. In this regard, theseismic data processing system 320 may, for example, store programinstructions 344 in a memory 340, which when executed by a processor 350cause the processor 350 (and system 320) to perform iterative numericaltechniques to invert a cost function for purposes of determining thephysical propagation parameters that are associated with a marineseismic survey.

In accordance with some embodiments of the invention, the processor 350may includes one or more microprocessors and/or microcontrollers. Theprocessor 350 may be located on a streamer 30 (FIG. 1), located on thevessel 20 or located at a land-based processing facility (as examples),depending on the particular embodiment of the invention.

The processor 350 may be coupled to a communication interface 360 forpurposes of receiving seismic data that corresponds to pressure andparticle motion measurements. Thus, in accordance with embodiments ofthe invention described herein, the processor 350, when executinginstructions stored in a memory of the seismic data processing system320, may receive multi-component data that is acquired bymulti-component seismic sensors while in tow. It is noted that,depending on the particular embodiment of the invention, themulti-component data may be data that is directly received from themulti-component seismic sensor as the data is being acquired (for thecase in which the processor 350 is part of the survey system, such aspart of the vessel or streamer) or may be multi-component data that waspreviously acquired by the seismic sensors while in tow and stored andcommunicated to the processor 350, which may be in a land-basedfacility, for example.

As examples, the interface 360 may be a USB serial bus interface, anetwork interface, a removable media (such as a flash card, CD-ROM,etc.) interface or a magnetic storage interface (IDE or SCSI interfaces,as examples). Thus, the interface 360 may take on numerous forms,depending on the particular embodiment of the invention.

In accordance with some embodiments of the invention, the interface 360may be coupled to the memory 340, which in addition to the programinstructions 344 may store, for example, various raw, intermediate andfinal data sets involved with the techniques 100 and/or 120, asindicated by reference numeral 348. The program instructions 344, whenexecuted by the processor 350, may cause the processor 350 to performone or more parts of the techniques that are disclosed herein, such asthe techniques 100 and/or 120 (as a more specific example) and displayresults of the processing (inversion results, for example) on a display(not shown) of the system 320.

While the present invention has been described with respect to a limitednumber of embodiments, those skilled in the art, having the benefit ofthis disclosure, will appreciate numerous modifications and variationstherefrom. It is intended that the appended claims cover all suchmodifications and variations as fall within the true spirit and scope ofthis present invention.

1. A method comprising: estimating propagation parameters associatedwith a seismic survey based at least in part on seismic signalmeasurements and noise measurements.
 2. The method of claim 1, whereinthe act of estimating comprises basing the estimation of the propagationparameters on statistics estimated from the noise measurements.
 3. Themethod of claim 1, further comprising: using the estimated propagationparameters to estimate an upgoing pressure wavefield generated duringthe survey.
 4. The method of claim 1, wherein the propagation parameterscomprise a parameter selected from a sea surface reflection coefficient,a streamer depth and a speed of sound.
 5. The method of claim 1, whereinthe act of estimating comprises determining noise covariance from theseismic signal measurements.
 6. The method of claim 5, wherein the actof determining the noise covariance comprises extracting the noisemeasurement from the seismic signal measurements.
 7. The method of claim1, wherein the seismic signal measurements comprise pressuremeasurements and particle motion measurements.
 8. The method of claim 1,wherein the seismic survey comprises a survey generated using a towedstreamer.
 9. The method of claim 1, wherein the seismic survey comprisesa survey generated using seismic sensors deployed on a seabed.
 10. Themethod of claim 1, wherein the act of estimating comprises: providing acost function in terms of the seismic signal measurements, the noisemeasurements and a propagation model that is a function of thepropagation parameters; and inverting the cost function for thepropagation parameters.
 11. The method of claim 10, wherein the act ofinverting comprises performing one of minimizing the cost function andmaximizing the cost function.
 12. The method of claim 10, wherein theact of inverting comprises determining an expected value of the costfunction.
 13. The method of claim 12, wherein the act of determining theexpected value comprises averaging results obtained for several sets ofthe measurements to approximate the expected value.
 14. The method ofclaim 12, wherein the act of determining the expected value comprisesintegrating the cost function over frequency and wave number space toestimate the expected value.
 15. The method of claim 14, furthercomprising: convolving an integrand with a low pass function beforeperforming the act of integrating.
 16. The method of claim 12, whereinthe act of inverting further comprises minimizing the expected value.17. A system comprising: an interface to receive data indicative ofseismic signal measurements and noise measurements associated with atowed seismic survey; and a processor to process the data to estimatepropagation parameters associated with the survey based at least in parton the seismic signal measurements and the noise measurements.
 18. Thesystem of claim 17, wherein the propagation parameters comprises aparameter selected from a sea surface reflection coefficient, a streamerdepth and a speed of sound.
 19. The system of claim 17, wherein theprocessor determines a noise covariance from the seismic signalmeasurements.
 20. The system of claim 19, wherein the processor extractsthe noise measurements from the seismic signal measurements.
 21. Thesystem of claim 17, wherein the seismic signal measurements comprisepressure measurements and particle motion measurements.
 22. The systemof claim 17, wherein the processor is adapted to: access a cost functionin terms of the seismic signal measurements, the noise measurements anda propagation model that is a function of the propagation parameters;and invert the cost function for the propagation parameters.
 23. Thesystem of claim 17, further comprising: a streamer comprising seismicsensors to acquire the seismic signal measurements and the noisemeasurements.
 24. The system of claim 23, wherein the processor is partof the streamer.
 25. The system of claim 23, further comprising: asurvey vessel to tow the streamer.
 26. The system of claim 25, whereinthe processor is located on the survey vessel.
 27. An article comprisinga computer accessible storage medium to store instructions that whenexecuted cause a processor-based system to: estimate propagationparameters associated with a towed seismic survey based at least in parton seismic signal measurements and noise measurements.
 28. The articleof claim 27, wherein the propagation parameters comprises a parameterselected from a sea surface reflection coefficient, a streamer depth anda speed of sound.
 29. The article of claim 27, wherein the instructionswhen executed cause the processor-based system to determine a noisecovariance from the seismic signal measurements.
 30. The article ofclaim 27, wherein the instructions when executed cause theprocessor-based system to extract the noise measurements from theseismic signal measurements.
 31. The article of claim 27, wherein theseismic signal measurements comprise pressure measurements and particlemotion measurements.
 32. The article of claim 27, wherein theinstructions when executed cause the processor-based system to: access acost function that is defined in terms of the seismic signalmeasurements, the noise measurements and a propagation model that is afunction of the propagation parameters; and invert the cost function forthe propagation parameters.